PhD Students - Do you need datasets for your research?

Here are 30 datasets for research from NexData.

Use discount code for 20% off: G5W924C3ZI

1. Korean Exam Question Dataset for AI Training

https://lnkd.in/d_paSwt7

2. Multilingual Grammar Correction Dataset

https://lnkd.in/dV43iqTp

3. High quality video caption dataset

https://lnkd.in/dY9kxkhx

4. 3D models and scenes datasets for AI and simulation

https://lnkd.in/dT-zscH4

5. Image editing datasets – object removal, addition & modification

https://lnkd.in/dd8iCGMS

6. QA dataset – visual & text reasoning

https://lnkd.in/dc3TNWFD

7. English instruction tuning dataset

https://lnkd.in/dTeTgd2M

8. Large scale vision language dataset for AI training

https://lnkd.in/dBJuxazN

9. News dataset

https://lnkd.in/dYBJe5gd

10. Global building photos dataset

https://lnkd.in/dVJsDXnC

11. Facial landmarks dataset

https://lnkd.in/dz_KGCS4

12. 3D Human Pose & Landmarks dataset

https://lnkd.in/dXE9ir8Z

13. 3D Hand Pose & Gesture Recognition dataset

https://lnkd.in/d_QdGGb9

14. 14. Driver monitoring dataset – dangerous, fatigue

https://lnkd.in/d6kF-9PW

15. Japanese handwriting OCR dataset

https://lnkd.in/dHnriqrH

16. American English Male voice TTS dataset

https://lnkd.in/dqyvg862

17. Riddles and brain teasers dataset

https://lnkd.in/dKBHY3DE

18. Chinese test questions text

https://lnkd.in/dQpUd8xC

19. Chinese medical question answering data

https://lnkd.in/dsbWUCpz

20. Multi-round interpersonal dialogues text data

https://lnkd.in/dQiUq_Jg

21. Human activity recognition dataset

https://lnkd.in/dHM52MfV

22. Facial expression recognition dataset

https://lnkd.in/dqQAfMau

23. Urban surveillance dataset

https://lnkd.in/dc2RCnTk

24. Human body segmentation dataset

https://lnkd.in/d6sSrDxS

25. Fashion segmentation – clothing & accessories

https://lnkd.in/dptNUTz8

26. Fight video dataset – action recognition

https://lnkd.in/dnY_m5hZ

27. Gesture recognition dataset

https://lnkd.in/dFVPivYg

28. Facial skin defects dataset

https://lnkd.in/dKCbUvU6

29. Smoke detection and behaviour recognition dataset

https://lnkd.in/ddGg56R4

30. Weight loss transformation video dataset

https://lnkd.in/dqqT4ed9

https://t.iss.one/CodeProgrammer 👾

🤖 Python libraries for AI agents — what to study

If you want to develop AI agents in Python, it's important to understand the order of studying libraries.

Start with LangChain, CrewAI or SmolAgents — they allow you to quickly assemble simple agents, connect tools, and test ideas.

The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows.

The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines.

LangChain — simple agents, tools, and memory 
github.com/langchain-ai/langchain

CrewAI — multi-agent systems with roles 
github.com/joaomdmoura/crewAI

SmolAgents — lightweight agents for quick experiments 
github.com/huggingface/smolagents

LangGraph — orchestration and stateful workflow 
github.com/langchain-ai/langgraph

LlamaIndex — RAG and knowledge-agents 
github.com/run-llama/llama_index

Semantic Kernel — AI workflow and plugins 
github.com/microsoft/semantic-kernel

AutoGen — autonomous multi-agent systems 
github.com/microsoft/autogen

DSPy — optimizing LLM pipelines 
github.com/stanfordnlp/dspy

A2A — protocol for interaction between agents 
github.com/a2aproject/A2A

https://t.iss.one/CodeProgrammer 🌟